Predictive flow-field estimation

Mokhasi, Paritosh; Rempfer, Dietmar; Kandala, Sriharsha

doi:10.1016/j.physd.2008.10.009

Cited by 28 publications

(10 citation statements)

References 55 publications

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“…In Eq. (10), the likelihood functions p(x|z), p(y|z) and the prior pdf p(z) appear, while only the posterior probabilities p(z|x) and p(z|y) are available in (6) and (7). To complete the model, the likelihood functions can be expressed in term of the posterior pdfs and prior of z using Bayesian rules.…”

Section: Map Estimationmentioning

confidence: 99%

A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements

Nguyen¹,

Laval²,

Chainais³

2015

J. Stat. Mech.

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The study of turbulent flows calls for measurements with high resolution both in space and in time. We propose a new approach to reconstruct High-Temporal-High-Spatial resolution velocity fields by combining two sources of information that are well-resolved either in space or in time, the Low-Temporal-High-Spatial (LTHS) and the High-Temporal-Low-Spatial (HTLS) resolution measurements. In the framework of co-conception between sensing and data post-processing, this work extensively investigates a Bayesian reconstruction approach using a simulated database. A Bayesian fusion model is developed to solve the inverse problem of data reconstruction. The model uses a Maximum A Posteriori estimate, which yields the most probable field knowing the measurements. The DNS of a wall-bounded turbulent flow at moderate Reynolds number is used to validate and assess the performances of the present approach. Low resolution measurements are subsampled in time and space from the fully resolved data. Reconstructed velocities are compared to the reference DNS to estimate the reconstruction errors. The model is compared to other conventional methods such as Linear Stochastic Estimation and cubic spline interpolation. Results show the superior accuracy of the proposed method in all configurations. Further investigations of model performances on various range of scales demonstrate its robustness. Numerical experiments also permit to estimate the expected maximum information level corresponding to limitations of experimental instruments.

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Section: Map Estimationmentioning

confidence: 99%

A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements

Nguyen¹,

Laval²,

Chainais³

2015

J. Stat. Mech.

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“…These methods include, e.g., adaptive control methods (Ravindran, 2000), the trust region approach Arian et al, 2000;Bergmann et al, 2007;Chen et al, 2009), and the episodal POD technique (Mokhasi and Rempfer, 2008;Mokhasi et al, 2009). To mitigate the proliferation of the expansion set for wide validity envelopes, yet another family of solutions is based on the adaptation of the expansion set on the fly.…”

Section: Extended Mode Setsmentioning

confidence: 99%

Reduced-Order Modelling for Flow Control

Noack¹,

Morzyński²,

Tadmor³

2011

CISM International Centre for Mechanical Sciences

192

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“…In addition, the high complexity of the flow dynamics makes solving these PDEs computationally expensive. Data-driven flow models (Mokhasi et al, 2009;Chang et al, 2014) can provide short-term flow prediction in a relatively smaller area with significantly lower computational cost, and can be more suitable for supporting real-time AUV navigation, particularly for systems with strong gradients and/or high uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Bounded Cost Path Planning for Underwater Vehicles Assisted by a Time-Invariant Partitioned Flow Field Model

et al. 2021

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A bounded cost path planning method is developed for underwater vehicles assisted by a data-driven flow modeling method. The modeled flow field is partitioned as a set of cells of piece-wise constant flow speed. A flow partition algorithm and a parameter estimation algorithm are proposed to learn the flow field structure and parameters with justified convergence. A bounded cost path planning algorithm is developed taking advantage of the partitioned flow model. An extended potential search method is proposed to determine the sequence of partitions that the optimal path crosses. The optimal path within each partition is then determined by solving a constrained optimization problem. Theoretical justification is provided for the proposed extended potential search method generating the optimal solution. The path planned has the highest probability to satisfy the bounded cost constraint. The performance of the algorithms is demonstrated with experimental and simulation results, which show that the proposed method is more computationally efficient than some of the existing methods.

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Predictive flow-field estimation

Cited by 28 publications

References 55 publications

A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements

A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements

Reduced-Order Modelling for Flow Control

Bounded Cost Path Planning for Underwater Vehicles Assisted by a Time-Invariant Partitioned Flow Field Model

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